from tensorflow import keras
from python_ai.common.xcommon import *

# 229,229,3 => 8,8,2048
# 520,520,3 => 14,14,2048
# 490 => 13
# 491~522 => 14
# 523 => 15

SIDE1 = 520

for SIDE in range(SIDE1, SIDE1+10):
    sep(SIDE)
    IMG_SHAPE = (SIDE, SIDE, 3)

    # Create the base model from the pre-trained model MobileNet V2
    base_model = keras.applications.InceptionV3(input_shape=IMG_SHAPE,
    # We cannot use the top classification layer of the pre-trained model as it contains 1000 classes.
    # It also restricts our input dimensions to that which this model is trained on (default: 299x299)
                                                   include_top=False,
                                                   weights='imagenet')

    last_layer = base_model.get_layer('mixed10')
    print('last layer output shape: ', last_layer.output_shape)
